Abstract
Visual geographic knowledge which can be extracted from satellite remote sensing images has characteristics which are not commonly found in non-visual domains. Traditionally geographic expert systems have worked either at the pixel level of raster images or the object level of vector images. This has shortfalls when knowledge acquisition from a human image interpreter has to be incorporated into an expert system to aid interpretation.
A framework for the classification of visual geographic knowledge will be presented that expands beyond the traditional per-pixel model and has been used as the theoretical basis of a knowledge acquisition toolkit, KAGES (Knowledge Acquisition for Geographic Expert Systems) [2]. This model will be compared with the KADS knowledge model to show the relationship with modeling in a non-visual environment.
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© 1999 Springer-Verlag Berlin Heidelberg
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Crowther, P. (1999). A Visual Geographic Knowledge Classification and Its Relationship to the KADS Model. In: Foo, N. (eds) Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science(), vol 1747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46695-9_45
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DOI: https://doi.org/10.1007/3-540-46695-9_45
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